Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 423–443, 2021
https://doi.org/10.5194/npg-28-423-2021
Nonlin. Processes Geophys., 28, 423–443, 2021
https://doi.org/10.5194/npg-28-423-2021
Research article
10 Sep 2021
Research article | 10 Sep 2021

Enhancing geophysical flow machine learning performance via scale separation

Davide Faranda et al.

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Cited articles

Bassett, D. and Sporns, O.: Network neuroscience, Nat. Neurosci., 20, 353–364, https://doi.org/10.1038/nn.4502, 2017. a
Berkson, J.: Minimum Chi-Square, not Maximum Likelihood!, Ann. Statist., 8, 457–487, https://doi.org/10.1214/aos/1176345003, 1980. a
Bolton, T. and Zanna, L.: Applications of deep learning to ocean data inference and subgrid parameterization, J. Adv. Model. Earth Sy., 11, 376–399, 2019. a
Bonferroni, C.: Teoria statistica delle classi e calcolo delle probabilita, Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commericiali di Firenze, 8, 3–62, 1936. a
Bony, S., Stevens, B., Frierson, D. M. W., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261–268, https://doi.org/10.1038/ngeo2398, 2015. a
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Machine learning approaches are spreading rapidly in climate sciences. They are of great help in many practical situations where using the underlying equations is difficult because of the limitation in computational power. Here we use a systematic approach to investigate the limitations of the popular echo state network algorithms used to forecast the long-term behaviour of chaotic systems, such as the weather. Our results show that noise and intermittency greatly affect the performances.